All radio communications systems can be subject to interfering signals, most notably, narrowband signals that interfere with wideband communications signals, for example, spread spectrum signals. A number of traditional systems and methods exist for removing or excising unwanted narrowband signals when they are in the presence of a wider bandwidth communications signal. Typically, there are in general two popular, but different approaches used by those skilled in the art to remove unwanted narrowband signals from a wider bandwidth signal. In a first approach, time domain adaptive filtering is used. In a second approach, frequency domain nulling is used. Both of these approaches end up corrupting the desired signal, especially as the communications industry moves towards wider bandwidth, higher bit rate waveforms. In these more complicated waveforms, the corruptions can end up limiting the usefulness of the desired signal. This is becoming increasingly apparent as advances are made in these wide bandwidth communications systems.
As is known by those skilled in the art, adaptive filtering takes advantage of the nature of narrowband signals, i.e., the correlation properties of these signals, which distinguish them from wide bandwidth information-bearing signals and noise signals. It is possible with adaptive filtering to suppress the narrowband components while only slightly affecting the wideband components (i.e., wideband signals and noise).
Thus, adaptive filtering can take advantage and exploit statistical properties of a sampled input signal. This can be accomplished, for example, by auto correlation, which refers to how recent samples of a waveform resemble any past input samples. It is also possible to use an adaptive predictor. Using a time delay and a primary and reference input, it is possible to form an auto correlation offset, representing the time difference used to compare past input samples with present samples. The amount of delay is chosen so that desired components and an input signal correlate with themselves and the desired end components do not.
LMS algorithms can also be used in conjunction with a Finite Impulse Response (FIR) filter for adjusting coefficient values and a delay relative to each other and other system components. A Finite Impulse Response filter can also be used in association with an error signal to train a series of adaptive taps to null any narrow bandwidth interference. These types of systems provide some advantageous suppression of interfering signals when a lower bit-rate and corresponding less-dense signal constellations are used. These systems do not work well, however, with higher bit-rate, denser constellations because the adaptive filter corrupts and removes desired receive signal energy. Also, some adaptive filters work well when the received communications signal and the jamming or interfering signal are unequal, i.e., the jamming or interfering signal is quite large compared to the received communications signal. If the two signals are about the same, the adaptive filtering technique often fails. Examples of prior art adaptive filtering, interference suppression systems are disclosed in U.S. Pat. No. 5,426,983 to Rakib et al. and U.S. Pat. No. 5,612,978 to Blanchard et al.
Other interference suppression systems have used Fourier transforms, for example, the Discrete Fourier Transform (DFT) and digital signal processing circuitry which sample signals as a block transform. These systems can convert a block of N input samples into a block of N output bins, which represent the frequency spectrum of the sampled signal. The frequency bins can have a real part and an imaginary part and sophisticated DSP calculations can involve a convolution sum with bins evenly spaced in frequency. For example, U.S. Pat. No. 6,246,729 to Richardson discloses a system that transforms windowed data into real and imaginary frequency components.
Discrete Fourier transforms can have rectangular, triangular, Blackman, Hamming and Hanning window functions. It is possible to reconstruct a time-domain signal using the Discrete Fourier Transform and the Inverse Discrete Fourier Transform (DFT/IDFT). This type of relationship does not alter the signal, but only alters the different ways the signal is represented mathematically.
Using the original equations which define the DFT, it is possible to exploit the symmetry in the computations and reduce computational cost significantly using decimation-in-time or decimation-in-frequency algorithms. These new transforms are referred to as the Fast Fourier Transforms (FFT).
In any FFT-based noise-reduction system, there could be some modification of the frequency-domain data, such as zeroing bins that exceed a predefined amplitude threshold or hard-limiting all or part of frequency domain bins. It is then possible to transform the modified data back to the time domain by performing an inverse FFT (IFFT). Although this approach provides some interference suppression, it may also corrupt the desired receive signal (where corruption effects include desired signal energy loss in zeroed bins). Corruption of the received signal is further increased when the interfering frequency is not centered on a bin. During processing, the interferer will spread over a number of frequency bins and elimination of the interferer will further corrupt the desired signal.